Tracking devices are known that sense and track user activities, especially sports activities. An example of a known activity tracking device is a wearable wristwatch device with a GPS receiver for tracking and analyzing ‘running’ activity of an associated user. Another example is a mobile application that utilizes GPS system of a respective mobile phone for recording movement of users while they exercise. Another example is a step counter used in shoes or attached to the user's clothes to collect the number of steps taken by the user. However, none of the existing tracking devices automatically sense, record, analyze and identify all types of user activities such as walking, running, jogging, cycling, rowing, driving with car, moving with bus, moving with train, walking stairs, running stairs, jumping, swimming, playing football, and skiing.
Nowadays, smartphones are equipped with an increasing number of sensors such as Global Positioning System (GPS) receivers, accelerometers, and proximity sensors, and smartphone users may find it interesting to have mobile applications that can automatically record, sense, analyze, and identify their activities. However, one of the key challenges in the automatic tracking of users' movements for the purpose of analyzing the type of activity is the classification of activity types. For example walking vs running activity may have only small difference in respect to the collected sensor data. Moreover, for the same activity, the sensor data may vary depending on how the smart phone is carried by the user. For example, the smartphone may be carried by the user in his/her hand, or in a pocket or in a backpack.
Hence, there is a need for an activity tracking solution that accurately senses and analyzes all kinds of user activities and that addresses the limitations of existing activity tracking solutions.